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| Section | Weight | Objectives |
|---|---|---|
| Implement natural language processing solutions | 15-20% | - Build conversational AI and chatbots - Customize and deploy NLP models - Implement translation and summarization - Perform text analysis, sentiment detection, and language detection |
| Plan and manage an Azure AI solution | 20-25% | - Select suitable AI models - Select appropriate Microsoft Foundry Services - Create and configure Azure AI resources - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Monitor, optimize, and secure AI solutions - Plan solutions aligned with responsible AI principles |
| Implement an agentic solution | 5-10% | - Understand agent use cases and types - Test, deploy, and optimize agents - Build agents with Microsoft Foundry Agent Service - Develop multi-agent workflows and orchestration |
| Implement generative AI solutions | 15-20% | - Orchestrate multiple models and containers - Deploy and manage generative models - Implement model monitoring and feedback - Integrate Azure OpenAI and other generative models - Apply prompt engineering and fine-tuning |
| Implement knowledge mining and information extraction solutions | 15-20% | - Implement intelligent search and retrieval - Ingest and process structured/unstructured data - Extract entities, relationships, and key phrases - Build knowledge bases and search indexes |
| Implement computer vision solutions | 10-15% | - Process and index video content - Analyze images and detect objects/features - Build and deploy custom vision models - Extract text and handwriting from images - Integrate vision capabilities into applications |
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NEW QUESTION # 80
You are building an Al solution that will use Sentiment Analysis results from surveys to calculate bonuses for customer service staff. You need to ensure that the solution meets the Microsoft responsible Al principles.
What should you do?
Answer: A
Explanation:
This question aligns with Microsoft Responsible AI principles (Fairness, Reliability & Safety, Privacy & Security, Inclusiveness, Transparency, and Accountability).
The scenario:
* Sentiment Analysis is used to determine staff bonuses (a financial impact).
* Automated sentiment classification can be biased or produce incorrect results.
* Decisions that affect people's financial or legal status should never be made fully automated.
Analysis of the options:
* A. Add a human review and approval step # Correct. This ensures accountability and fairness, reducing harm from potential model bias or errors.
* B. Include results even with low confidence # Wrong. That increases risk of incorrect decisions.
* C. Use all surveys including deleted accounts # Wrong. Violates privacy and compliance.
* D. Publish raw survey data # Wrong. Violates privacy and security principles.
Therefore, the best responsible AI-compliant action is to add human review before making financial decisions.
Correct Answer for Q186: A
* Microsoft Responsible AI principles
* Responsible use of AI in decision-making
NEW QUESTION # 81
You have an app that uses Azure Al and a custom trained classifier to identity products in images. You need to add new products to the classifier. The solution must meet the following requirements:
* Minimize how long it takes to add the products
* Minimize development effort.
Which five actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
To quickly add new product classes to an existing image classifier with minimal development effort, use the Custom Vision portal workflow. Vision Studio and Azure Machine Learning studio are not required for Custom Vision projects. The standard lifecycle to update a classifier is: open the project, add/upload images, label them with the correct tags, retrain to incorporate the new labels, and publish the updated iteration so the app can consume the new model via the Prediction endpoint.
Microsoft Azure AI Solution References
* Azure AI Services - Custom Vision : add images, tag, train, and publish iterations from the portal.
* Custom Vision documentation - Quickstart: Build a classifier in the Custom Vision website (steps:
create/open project, upload images, tag, train, publish).
NEW QUESTION # 82
You are building a model that will be used in an iOS app.
You have images of cats and dogs. Each image contains either a cat or a dog.
You need to use the Custom Vision service to detect whether the images is of a cat or a dog.
How should you configure the project in the Custom Vision portal? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Classification
Box 2: Multiclass
A multiclass classification project is for classifying images into a set of tags, or target labels. An image can be assigned to one tag only.
Box 3: General
General: Optimized for a broad range of image classification tasks. If none of the other specific domains are appropriate, or if you're unsure of which domain to choose, select one of the General domains.
Reference:
https://cran.r-project.org/web/packages/AzureVision/vignettes/customvision.html
NEW QUESTION # 83
You build a bot by using the Microsoft Bot Framework SDK.
You need to test the bot interactively on a local machine.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.
Answer:
Explanation:
Explanation:
Comprehensive Detailed Explanation The question asks about testing a bot interactively on a local machine when using the Microsoft Bot Framework SDK.
Steps to test locally:
* Build and run the bot
* Compile the bot application code and run it locally (e.g., using Visual Studio or Node.js).
* This exposes the bot's endpoint on localhost (commonly http://localhost:3978/api/messages ).
* Open the Bot Framework Emulator
* The Bot Framework Emulator is a desktop app for testing and debugging bots built with the Bot Framework SDK.
* It allows you to send messages to the bot and see the responses.
* Connect to the bot endpoint
* Use the Emulator to connect to the locally running bot by entering its endpoint URL.
* Once connected, you can interact with the bot as if you were a real user.
* Register the bot with the Azure Bot Service # Needed for cloud deployment, not local testing.
* Open the Bot Framework Composer # Only needed if designing the bot with Composer, but this scenario uses the SDK directly.
Actions Not Required in This Case
* Build and run the bot # Open the Bot Framework Emulator # Connect to the bot endpoint Correct Sequences (multiple possible valid orders):
* Test a bot locally with Bot Framework Emulator
* Bot Framework SDK overview
Microsoft References
# Final Answer Sequence:
* Build and run the bot
* Open the Bot Framework Emulator
* Connect to the bot endpoint
NEW QUESTION # 84
You are developing an application that will recognize faults in components produced on a factory production line. The components are specific to your business.
You need to use the Custom Vision API to help detect common faults.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
Comprehensive Detailed Explanation You are tasked with building a solution using Custom Vision to detect faults in factory components. Custom Vision follows a defined workflow:
* Create a project
* Before anything else, you must create a Custom Vision project in the Azure AI portal.
* This project specifies the type of model you are building: classification (classifying entire images) or object detection (locating items within images).
* Since the question is about detecting faults in specific components (a business-specific scenario), it aligns with classification unless explicitly stated otherwise.
* Upload and tag images
* You must provide training data.
* Upload a set of images showing both faulty and non-faulty components.
* Tag each image correctly so that the model can learn from labeled examples.
* Train the classifier model
* Once images are tagged, you run training on the dataset to generate the classifier model.
* The model can then be published and used for inference via API calls.
* Initialize the training dataset: This is not a direct step in Custom Vision; instead, uploading and tagging images inherently prepares the training dataset.
* Train the object detection model: Object detection is used if you need bounding boxes around items within an image. Since the question is about recognizing faults in a component (faulty vs non-faulty), a classifier model is appropriate.
Why not the other options?
Correct Sequence:
* Create a project
* Upload and tag images
* Train the classifier model
* Quickstart: Build an image classification project with the Custom Vision portal
* Custom Vision overview
* Train and test a Custom Vision model
Microsoft References
NEW QUESTION # 85
......
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